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A fractional order moments-based detector is proposed for the detection of weak signals in additive impulsive noise environment assumed as generalized Gaussian distribution with properly selected parameter values. The asymptotic detection performance is derived and compared with some traditional detectors optimized for operations in Gaussian noise with Nakagami fading communication channels. The analytical and computer simulation results of the fractional order moment-based detector are shown for signal detection with fading channels in the impulsive noise.

Clustering organizes nodes into groups in order to enhance the connectivity and stability of cognitive radio sensor networks. Mainly depending on the channel availability, many existing spectrum-aware clustering algorithms may not achieve the most satisfactory clustering. Taking into account the various influence factors to establish the optimal clustering is a challenge to enhance the network performance. This paper proposes a novel spectrum-aware clustering algorithm based on weighted clustering metric to obtain the optimal clustering by solving an optimization model. The new weighted clustering metric, simultaneously evaluating temporal-spatial correlation, confidence level and residual energy, is used to elect clusterheads and ally member nodes. After clustering, the clusterheads sensing spectrum instead of all member nodes greatly reduces the energy consumption of spectrum sensing and increases the opportunity of data transmission. The performance comparison between the traditional spectrum-aware clustering algorithms and our proposed algorithm has been highlighted with the experiments.

Target localization is one of the essential tasks in the applications of wireless sensor networks (WSNs). The traditional target localization based on received signal strength may fail to obtain satisfactory localization performance, especially when the number of the RSS measurements is limited. Compressed sensing (CS) has been shown to be an effective technique for target localization due to the intrinsic sparse nature of target localization in WSNs. The CS-based target localization can be formulated to a sparse recovery problem based on $l_{0} $ -norm or $l_{1} $ -norm minimization. Compared to $l_{0} $ -norm and $l_{1}$ -norm, $l_{p} $ -norm $(0 can provide the most effective sparsity measurement of a vector. Some traditional sparse recovery algorithms for $l_{p} $ -norm minimization, however, usually obtain suboptimal sparse solutions when the initial point is not in the convergence domain of the globally optimal sparse solution. In this paper, we propose a novel affine scaling steepest descent (ASSD) algorithm to find a satisfying sparse solution of $l_{p} $ -norm minimization. By setting an optimal stepsize at each iteration, our ASSD algorithm can avoid the iterative solutions concentrating on the attraction basin of the suboptimal sparse solution and move to the attraction basin of a sparser solution, so it has high opportunity to obtain the globally optimal sparse solution, and then accurately determine the locations of targets. The experimental results show that our ASSD algorithm performs much better than the traditional BP, OMP, GMP, ASM, IRL1, and ITM algorithms, especially when the number of measurements is insufficient.

Incorporating link correlation awareness into wireless network protocols to facilitate data transmission is an important research issue. In this paper, we focus on link correlation based security threat and countermeasure in wireless networks. By taking advantage of the vulnerability of beacon-based link correlation measurement and the blind spot of malicious node detection mechanisms, we design a new type of link correlation spoofing attack (LCSA) to decrease protocol performance by distorting link correlation information while escaping the tracking of any watchdog and trust systems. Typical cases are analyzed to quantify how the LCSA covertly weakens protocol performance. We also propose beacon-trap (BT), a countermeasure embedded in the beacon-based link condition measurement protocol. Using link diversity as a cover, BT sets traps in the beacon sending sequence to ambush malicious nodes that launch LCSAs without extra control overhead. The performance of BT is not affected by changes in the size of a network or the distribution of nodes. Numerical results demonstrate the superiority and effectiveness of BT against LCSAs in terms of malicious node detection success rate and speed under different parameter settings.

Wireless cognitive sensor networks arranged in smart community intelligently use the idle channels that alleviates spectrum scarcity. To enhance the connectivity of network, the existing spectrum-aware clustering algorithms group sensors into clusters due to the sensed idle channels of each sensor. However, a clustering metric should depend on various evaluation criteria. This poster proposes a novel spectrum-aware clustering algorithm to obtain more reasonable clustering, where a new weighted clustering metric is used to elect clusterheads. After clustering, the clusterheads sense spectrum instead of all nodes that greatly reduces the energy consumption and efficiently increase the network throughout.

The grid-based target localization for internet of vehicle has the property of sparsity, which can be transformed to a sparse recovery problem due to compressive sensing. The accurate target localization relies on the sufficient measurements, but we cannot determine the number of measurements without knowing the number of targets. In this poster, we propose a novel target localization algorithm based on sequential compressed sensing to select the optimal number of measurements and estimate target locations via l p -norm (0 0 -norm or l 1 -norm minimization.

Aiming to minimize the operators' cost while preserving user experience, we propose a resource scheduling mechanism for cooperative cloudlets in edge computing with a centralized controller. The interactions between cloudlets and the controller are formulated as a two-stage Stackelberg game to determine the amount of physical resources assigned to each cloudlet during deployment phase and the price of resources shared among cooperated cloudlets during operation phase.

Wireless sensor network is becoming increasingly important in applications such as environmental monitoring and traffic control. They collect a large amount of sensor data, e.g., temperature and pressure. Nearby sensor nodes monitoring an environmental feature typically measure correlated values. In this paper, we propose a new location-aware-based data clustering (LABDC) algorithm to analyze the spatial correlation of sensor data. LABDC is a lossy mechanism that reduces the number of transmissions and provides approximate query results to users. Without using real-time sensor data, LABDC performs data clustering tasks based on the user-provided error-tolerance threshold and the sensor data dissimilarity matrix. Subsequently, only one node per cluster is selected as the clusterhead by maximal remainder energy. Mobile agent collects data of clusterheads. Mathematical models and simulations are used to evaluate the energy efficiency and correctness of LABDC. Extensive experiments show that LABDC largely reduces transmission costs compared to other data clustering algorithms.

Compressed sensing is a new concept in signal processing where one seeks to minimize the number of measurements to be taken from signals while still retaining the information necessary to approximate them well. Nonlinear algorithms, such as l1 norm optimization problem, are used to reconstruct the signal from the measured data. This paper proposes a maximum entropy function method which intimately relates to homotopy method as a computational approach to solve the l1 optimization problem. Maximum entropy function method makes it possible to design random measurements which contain the information necessary to reconstruct signal with accuracy. Both the theoretical evidences and the extensive experiments show that it is an effective technique for signal reconstruction. This approach offers several advantages over other methods, including scalability and robustness.